> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cloudthinker.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Recommendations
> AI-generated cost optimization recommendations with prioritization and tracking
The Recommendation Engine is the core of CloudThinker's cost optimization system. It analyzes your infrastructure, identifies savings opportunities, and generates actionable recommendations with clear implementation paths.
***
## The Problem With Cost Recommendations Today
Most cloud tools generate recommendations — AWS Trusted Advisor shows underutilized EC2 instances; Cost Explorer suggests reserved instance purchases. But these recommendations come with no context, no prioritization logic, no effort assessment, and no implementation guidance. The result: engineers get a list of 200 recommendations, spend an hour trying to understand which matter, and implement maybe 5% of them.
**What's missing:**
* Dollar-value impact (not just percentages)
* Implementation effort and risk — a recommendation that saves \$50/month but requires 8 hours to implement safely is a bad use of time
* Implementation steps — engineers spend hours figuring out *how* to implement what the tool identifies
* Lifecycle tracking — no way to know if a recommendation from 3 months ago was implemented
* Team collaboration — no discussion thread, no ownership assignment, no status tracking
CloudThinker's Recommendation Engine provides all of this, turning a list of suggestions into a managed optimization workflow.
***
## Recommendation Attributes
Each recommendation includes comprehensive metadata to help you prioritize and implement:
| Attribute | Description |
| --------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
| **Title** | Clear, actionable summary of the optimization |
| **Description** | Detailed explanation of the issue and solution |
| **Potential Savings** | Estimated monthly or annual savings (high precision) |
| **Effort Level** | Implementation complexity: Low, Medium, or High |
| **Risk Level** | Potential impact on workloads: Low, Medium, or High |
| **Source** | Origin: [Assessment](/guide/infrastructure/assessment), Conversation, Manual, or [CloudKeepers](/guide/infrastructure/cloudkeepers) |
| **Status** | Current state: Pending, In Progress, Implemented, or Ignored |
| **Visibility** | Workflow state: Draft, Active, or Archived |
***
## Recommendation Lifecycle
[Alex](/guide/agents/alex) or [CloudKeepers](/guide/infrastructure/cloudkeepers) identifies an optimization opportunity and creates a recommendation in **Draft** status.
Review and promote recommendations to **Active** to make them visible to your team and trackable.
Mark recommendations as **In Progress** when implementation begins.
After completion, mark as **Implemented** to track actual savings against projections.
Dismiss recommendations that aren't applicable with an optional reason.
***
## Viewing Recommendations
### Dashboard View
Access recommendations from the main dashboard to see:
* High-priority recommendations by potential savings
* Recommendations grouped by resource type
* Implementation status overview
* Total potential and implemented savings
### Conversation-Based Discovery
Ask [Alex](/guide/agents/alex) to find and explain recommendations:
```bash theme={null}
# List top recommendations
@alex show top 10 cost recommendations by savings
# Filter by resource type
@alex what EC2 optimization opportunities exist?
# Filter by effort
@alex show low-effort recommendations with high savings
# Explain a specific recommendation
@alex explain why we should resize instance i-0abc123
```
***
## Creating Recommendations
Recommendations are created through multiple sources:
### 1. Automated Analysis
[CloudKeepers](/guide/infrastructure/cloudkeepers) continuously monitors your infrastructure and generates recommendations based on:
* Resource utilization patterns
* Cost anomalies
* Best practice violations
* Spending trends
### 2. Agent Conversations
Ask [Alex](/guide/agents/alex) to analyze specific areas:
```bash theme={null}
@alex analyze our S3 storage costs and recommend optimizations
@alex review EC2 instances that have been idle for over 7 days
```
### 3. Well-Architected Assessments
Recommendations are generated during [Well-Architected Framework assessments](/guide/infrastructure/assessment) under the Cost Optimization pillar.
### 4. Manual Creation
Create recommendations manually for custom optimization opportunities:
* Navigate to Recommendations in your workspace
* Click "New Recommendation"
* Fill in the recommendation details
* Assign effort, risk, and savings estimates
***
## Recommendation Categories
### Compute Optimization
Identify instances with consistently low CPU/memory utilization that can be downsized:
* EC2 instances under 20% average utilization
* RDS instances with excess capacity
* Lambda functions with over-provisioned memory
Recommendations for converting on-demand to reserved instances:
* 1-year vs 3-year commitment analysis
* Savings Plans coverage gaps
* Reserved instance utilization optimization
Identify workloads suitable for Spot instances:
* Fault-tolerant batch jobs
* Development/test environments
* Stateless applications
### Storage Optimization
Automate storage tier transitions:
* S3 Intelligent Tiering enablement
* Glacier archive recommendations
* Infrequently accessed data identification
Identify and cleanup:
* Unattached EBS volumes
* Orphaned snapshots
* Empty S3 buckets
* Unused EFS file systems
### Database Optimization
* Over-provisioned RDS instances
* DynamoDB capacity mode recommendations
* ElastiCache node optimization
* Index recommendations for query performance
* Query optimization suggestions
* Read replica opportunities
### Network Optimization
* Cross-region transfer optimization
* NAT Gateway efficiency
* VPC endpoint recommendations
* Idle load balancer detection
* ALB to NLB migration opportunities
* Cross-zone load balancing optimization
***
## Discussion & Collaboration
Each recommendation includes a discussion thread for team collaboration:
* **Comments**: Add context, questions, or implementation notes
* **Mentions**: Tag team members with @username
* **Attachments**: Link related documents or tickets
* **Audit Trail**: Track all changes and status updates
***
## Implementing Recommendations
### With Approval
For recommendations requiring infrastructure changes:
1. Review the recommendation details
2. Request implementation from [Alex](/guide/agents/alex)
3. Review the proposed changes
4. Approve execution in the [Approval Center](/guide/approval)
5. Monitor implementation progress
```bash theme={null}
@alex implement recommendation for resizing instance i-0abc123
# Alex will show the proposed changes and request approval
```
### Manual Implementation
For recommendations you want to implement yourself:
1. Mark as "In Progress"
2. Follow the implementation steps provided
3. Use the discussion thread for questions
4. Mark as "Implemented" when complete
5. Add actual savings for tracking
***
## Tracking & Reporting
### Savings Dashboard
Track optimization progress with:
* Total potential savings identified
* Savings implemented vs. available
* Savings trend over time
* Implementation velocity
### Export & Integration
* Export recommendations to CSV/Excel
* Create Jira tickets from recommendations
* Sync with external tracking systems via [webhooks](/guide/webhooks/overview)
* Generate executive reports
Send recommendation updates to external systems